This repository implements the LOFT approach described in the IROS 2021 paper:
Learning Symbolic Operators for Task and Motion Planning
Tom Silver*, Rohan Chitnis*, Joshua Tenenbaum, Leslie Pack Kaelbling, Tomas Lozano-Perez
Link to paper: https://arxiv.org/abs/2103.00589
Instructions for running (tested on OS X and Ubuntu 18.04):
- Use Python 3.6 or higher.
- Download Python dependencies:
pip install -r requirements.txt. - Download the NDR package to a location on your path: https://github.com/tomsilver/ndr
Now, ./run.sh should work, and should finish in less than a second.
You should see the printout In total, solved 30 / 30 near the end.
The three different environments can be run by changing the ENV
variable in run.sh. Data has been included already in the data/
folder, but if you would like to regenerate it, you can set
COLLECT_DATA=1 in run.sh. All three environments should yield 100%
test success rate on all seeds.
For questions or comments, please email tslvr@mit.edu and ronuchit@mit.edu.